Often I find myself having to represent a structure that consists of very small values. For example, Foo has 4 values, a, b, c, d that, range from 0 to 3. Usually I don't care, but sometimes, those structures are
used in a tight loop;
their values are read a billion times/s, and that is the bottleneck of the program;
the whole program consists of a big array of billions of Foos;
In that case, I find myself having trouble deciding how to represent Foo efficiently. I have basically 4 options:
struct Foo {
int a;
int b;
int c;
int d;
};
struct Foo {
char a;
char b;
char c;
char d;
};
struct Foo {
char abcd;
};
struct FourFoos {
int abcd_abcd_abcd_abcd;
};
They use 128, 32, 8, 8 bits respectively per Foo, ranging from sparse to densely packed. The first example is probably the most linguistic one, but using it would essentially increase by 16 times the size of the program, which doesn't sound quite right. Moreover, most of the memory will be filled with zeroes and not be used at all, which makes me wonder if this isn't a waste. On the other hands, packing them densely brings an additional overhead for of reading them.
What is the computationally 'fastest' method for representing small values in a struct?
For dense packing that doesn't incur a large overhead of reading, I'd recommend a struct with bitfields. In your example where you have four values ranging from 0 to 3, you'd define the struct as follows:
struct Foo {
unsigned char a:2;
unsigned char b:2;
unsigned char c:2;
unsigned char d:2;
}
This has a size of 1 byte, and the fields can be accessed simply, i.e. foo.a, foo.b, etc.
By making your struct more densely packed, that should help with cache efficiency.
Edit:
To summarize the comments:
There's still bit fiddling happening with a bitfield, however it's done by the compiler and will most likely be more efficient than what you would write by hand (not to mention it makes your source code more concise and less prone to introducing bugs). And given the large amount of structs you'll be dealing with, the reduction of cache misses gained by using a packed struct such as this will likely make up for the overhead of bit manipulation the struct imposes.
Pack them only if space is a consideration - for example, an array of 1,000,000 structs. Otherwise, the code needed to do shifting and masking is greater than the savings in space for the data. Hence you are more likely to have a cache miss on the I-cache than the D-cache.
There is no definitive answer, and you haven't given enough information to allow a "right" choice to be made. There are trade-offs.
Your statement that your "primary goal is time efficiency" is insufficient, since you haven't specified whether I/O time (e.g. to read data from file) is more of a concern than computational efficiency (e.g. how long some set of computations take after a user hits a "Go" button).
So it might be appropriate to write the data as a single char (to reduce time to read or write) but unpack it into an array of four int (so subsequent calculations go faster).
Also, there is no guarantee that an int is 32 bits (which you have assumed in your statement that the first packing uses 128 bits). An int can be 16 bits.
Foo has 4 values, a, b, c, d that, range from 0 to 3. Usually I don't
care, but sometimes, those structures are ...
There is another option: since the values 0 ... 3 likely indicate some sort of state, you could consider using "flags"
enum{
A_1 = 1<<0,
A_2 = 1<<1,
A_3 = A_1|A_2,
B_1 = 1<<2,
B_2 = 1<<3,
B_3 = B_1|B_2,
C_1 = 1<<4,
C_2 = 1<<5,
C_3 = C_1|C_2,
D_1 = 1<<6,
D_2 = 1<<7,
D_3 = D_1|D_2,
//you could continue to ... D7_3 for 32/64 bits if it makes sense
}
This isn't much different than using bitfields for most situations, but can drastically reduce your conditional logic.
if ( a < 2 && b < 2 && c < 2 && d < 2) // .... (4 comparisons)
//vs.
if ( abcd & (A_2|B_2|C_2|D_2) !=0 ) //(bitop with constant and a 0-compare)
Depending what kinds of operations you will be doing on the data, it may make sense to use either 4 or 8 sets of abcd and pad out the end with 0s as needed. That could allow up to 32 comparisons to be replaced with a bitop and 0-compare.
For instance, if you wanted to set the "1 bit" on all 8 sets of 4 in a 64 bit variable you can do uint64_t abcd8 = 0x5555555555555555ULL; then to set all the 2 bits you could do abcd8 |= 0xAAAAAAAAAAAAAAAAULL; making all values now 3
Addendum:
On further consideration, you could use a union as your type and either do a union with char and #dbush's bitfields (these flag operations would still work on the unsigned char) or use char types for each a,b,c,d and union them with unsigned int. This would allow both a compact representation and efficient operations depending on what union member you use.
union Foo {
char abcd; //Note: you can use flags and bitops on this too
struct {
unsigned char a:2;
unsigned char b:2;
unsigned char c:2;
unsigned char d:2;
};
};
Or even extended further
union Foo {
uint64_t abcd8; //Note: you can use flags and bitops on these too
uint32_t abcd4[2];
uint16_t abcd2[4];
uint8_t abcd[8];
struct {
unsigned char a:2;
unsigned char b:2;
unsigned char c:2;
unsigned char d:2;
} _[8];
};
union Foo myfoo = {0xFFFFFFFFFFFFFFFFULL};
//assert(myfoo._[0].a == 3 && myfoo.abcd[0] == 0xFF);
This method does introduce some endianness differences, which would also be a problem if you use a union to cover any other combination of your other methods.
union Foo {
uint32_t abcd;
uint32_t dcba; //only here for endian purposes
struct { //anonymous struct
char a;
char b;
char c;
char d;
};
};
You could experiment and measure with different union types and algorithms to see which parts of the unions are worth keeping, then discard the ones that are not useful. You may find that operating on several char/short/int types simultaneously gets automatically optimized to some combination of AVX/simd instructions whereas using bitfields does not unless you manually unroll them... there is no way to know until you test and measure them.
Fitting your data set in cache is critical. Smaller is always better, because hyperthreading competitively shares the per-core caches between the hardware threads (on Intel CPUs). Comments on this answer include some numbers for costs of cache misses.
On x86, loading 8bit values with sign or zero-extension into 32 or 64bit registers (movzx or movsx) is literally just as fast as plain mov of a byte or 32bit dword. Storing the low byte of a 32bit register also has no overhead. (See Agner Fog's instruction tables and C / asm optimization guides here).
Still x86-specific: [u]int8_t temporaries are ok, too, but avoid [u]int16_t temporaries. (load/store from/to [u]int16_t in memory is fine, but working with 16bit values in registers has big penalties from the operand-size prefix decoding slowly on Intel CPUs.) 32bit temporaries will be faster if you want to use them as an array index. (Using 8bit registers doesn't zero the high 24/56bits, so it takes an extra instruction to zero or sign-extend, to use an 8bit register as an array index, or in an expression with a wider type (like adding it to an int.)
I'm unsure what ARM or other architectures can do as far as efficient zero/sign extension from single-byte loads, or for single-byte stores.
Given this, my recommendation is pack for storage, use int for temporaries. (Or long, but that will increase code size slightly on x86-64, because a REX prefix is needed to specify a 64bit operand size.) e.g.
int a_i = foo[i].a;
int b_i = foo[i].b;
...;
foo[i].a = a_i + b_i;
bitfields
Packing into bitfields will have more overhead, but can still be worth it. Testing a compile-time-constant-bit-position (or multiple bits) in a byte or 32/64bit chunk of memory is fast. If you actually need to unpack some bitfields into ints and pass them to a non-inline function call or something, that will take a couple extra instructions to shift and mask. If this gives even a small reduction in cache misses, this can be worth it.
Testing, setting (to 1) or clearing (to 0) a bit or group of bits can be done efficiently with OR or AND, but assigning an unknown boolean value to a bitfield takes more instructions to merge the new bits with the bits for other fields. This can significantly bloat code if you assign a variable to a bitfield very often. So using int foo:6 and things like that in your structs, because you know foo doesn't need the top two bits, is not likely to be helpful. If you're not saving many bits compared to putting each thing in it's own byte/short/int, then the reduction in cache misses won't outweigh the extra instructions (which can add up into I-cache / uop-cache misses, as well as the direct extra latency and work of the instructions.)
The x86 BMI1 / BMI2 (Bit-Manipulation) instruction-set extensions will make copying data from a register into some destination bits (without clobbering the surrounding bits) more efficient. BMI1: Haswell, Piledriver. BMI2: Haswell, Excavator(unreleased). Note that like SSE/AVX, this will mean you'd need BMI versions of your functions, and fallback non-BMI versions for CPUs that don't support those instructions. AFAIK, compilers don't have options to see patterns for these instructions and use them automatically. They're only usable via intrinsics (or asm).
Dbush's answer, packing into bitfields is probably a good choice, depending on how you use your fields. Your fourth option (of packing four separate abcd values into one struct) is probably a mistake, unless you can do something useful with four sequential abcd values (vector-style).
code generically, try both ways
For a data structure your code uses extensively, it makes sense to set things up so you can flip from one implementation to another, and benchmark. Nir Friedman's answer, with getters/setters is a good way to go. However, just using int temporaries and working with the fields as separate members of the struct should work fine. It's up to the compiler to generate code to test the right bits of a byte, for packed bitfields.
prepare for SIMD, if warranted
If you have any code that checks just one or a couple fields of each struct, esp. looping over sequential struct values, then the struct-of-arrays answer given by cmaster will be useful. x86 vector instructions have a single byte as the smallest granularity, so a struct-of-arrays with each value in a separate byte would let you quickly scan for the first element where a == something, using PCMPEQB / PTEST.
First, precisely define what you mean by "most efficient". Best memory utilization? Best performance?
Then implement your algorithm both ways and actually profile it on the actual hardware you intend to run it on under the actual conditions you intend to run it under once it's delivered.
Pick the one that better meets your original definition of "most efficient".
Anything else is just a guess. Whatever you choose will probably work fine, but without actually measuring the difference under the exact conditions you'd use the software, you'll never know which implementation would be "more efficient".
I think the only real answer can be to write your code generically, and then profile the full program with all of them. I don't think this will take that much time, though it may look a little more awkward. Basically, I'd do something like this:
template <bool is_packed> class Foo;
using interface_int = char;
template <>
class Foo<true> {
char m_a, m_b, m_c, m_d;
public:
void setA(interface_int a) { m_a = a; }
interface_int getA() { return m_a; }
...
}
template <>
class Foo<false> {
char m_data;
public:
void setA(interface_int a) { // bit magic changes m_data; }
interface_int getA() { // bit magic gets a from m_data; }
}
If you just write your code like this instead of exposing the raw data, it will be easy to switch implementations and profile. The function calls will get inlined and will not impact performance. Note that I just wrote setA and getA instead of a function that returns a reference, this is more complicated to implement.
Code it with ints
treat the fields as ints.
blah.x in all your code, except the declarion will be all you will be doing. Integral promotion will take care of most cases.
When you are all done, have 3 equivalant include files: an include file using ints, one using char and one using bitfields.
And then profile. Don't worry about it at this stage, because its premature optimization, and nothing but your chosen include file will change.
Massive Arrays and Out of Memory Errors
the whole program consists of a big array of billions of Foos;
First things first, for #2, you might find yourself or your users (if others run the software) often being unable to allocate this array successfully if it spans gigabytes. A common mistake here is to think that out of memory errors mean "no more memory available", when they instead often mean that the OS could not find a contiguous set of unused pages matching the requested memory size. It's for this reason that people often get confused when they request to allocate a one gigabyte block only to have it fail even though they have 30 gigabytes of physical memory free, e.g. Once you start allocating memory in sizes that span more than, say, 1% of the typical amount of memory available, it's often time to consider avoiding one giant array to represent the whole thing.
So perhaps the first thing you need to do is rethink the data structure. Instead of allocating a single array of billions of elements, often you'll significantly reduce the odds of running into problems by allocating in smaller chunks (smaller arrays aggregated together). For example, if your access pattern is solely sequential in nature, you can use an unrolled list (arrays linked together). If random access is needed, you might use something like an array of pointers to arrays which each span 4 kilobytes. This requires a bit more work to index an element, but with this kind of scale of billions of elements, it's often a necessity.
Access Patterns
One of the things unspecified in the question are the memory access patterns. This part is critical for guiding your decisions.
For example, is the data structure solely traversed sequentially, or is random access needed? Are all of these fields: a, b, c, d, needed together all the time, or can they be accessed one or two or three at a time?
Let's try to cover all the possibilities. At the scale we're talking about, this:
struct Foo {
int a1;
int b1;
int c1;
int d1
};
... is unlikely to be helpful. At this kind of input scale, and accessed in tight loops, your times are generally going to be dominated by the upper levels of memory hierarchy (paging and CPU cache). It no longer becomes quite as critical to focus on the lowest level of the hierarchy (registers and associated instructions). To put it another way, at billions of elements to process, the last thing you should be worrying about is the cost of moving this memory from L1 cache lines to registers and the cost of bitwise instructions, e.g. (not saying it's not a concern at all, just saying it's a much lower priority).
At a small enough scale where the entirety of the hot data fits into the CPU cache and a need for random access, this kind of straightforward representation can show a performance improvement due to the improvements at the lowest level of the hierarchy (registers and instructions), yet it would require a drastically smaller-scale input than what we're talking about.
So even this is likely to be a considerable improvement:
struct Foo {
char a1;
char b1;
char c1;
char d1;
};
... and this even more:
// Each field packs 4 values with 2-bits each.
struct Foo {
char a4;
char b4;
char c4;
char d4;
};
* Note that you could use bitfields for the above, but bitfields tend to have caveats associated with them depending on the compiler being used. I've often been careful to avoid them due to the portability issues commonly described, though this may be unnecessary in your case. However, as we adventure into SoA and hot/cold field-splitting territories below, we'll reach a point where bitfields can't be used anyway.
This code also places a focus on horizontal logic which can start to make it easier to explore some further optimization paths (ex: transforming the code to use SIMD), as it's already in a miniature SoA form.
Data "Consumption"
Especially at this kind of scale, and even more so when your memory access is sequential in nature, it helps to think in terms of data "consumption" (how quickly the machine can load data, do the necessary arithmetic, and store the results). A simple mental image I find useful is to imagine the computer as having a "big mouth". It goes faster if we feed it large enough spoonfuls of data at once, not little teeny teaspoons, and with more relevant data packed tightly into a contiguous spoonful.
Hot/Cold Field Splitting
The above code so far is making the assumption that all of these fields are equally hot (accessed frequently), and accessed together. You may have some cold fields or fields that are only accessed in critical code paths in pairs. Let's say that you rarely access c and d, or that your code has one critical loop that accesses a and b, and another that accesses c and d. In that case, it can be helpful to split it off into two structures:
struct Foo1 {
char a4;
char b4;
};
struct Foo2 {
char c4;
char d4;
};
Again if we're "feeding" the computer data, and our code is only interested in a and b fields at the moment, we can pack more into spoonfuls of a and b fields if we have contiguous blocks that only contain a and b fields, and not c and d fields. In such a case, c and d fields would be data the computer can't digest at the moment, yet it would be mixed into the memory regions in between a and b fields. If we want the computer to consume data as quickly as possible, we should only be feeding it the relevant data of interest at the moment, so it's worth splitting the structures in these scenarios.
SIMD SoA for Sequential Access
Moving towards vectorization, and assuming sequential access, the fastest rate at which the computer can consume data will often be in parallel using SIMD. In such a case, we might end up with a representation like this:
struct Foo1 {
char* a4n;
char* b4n;
};
... with careful attention to alignment and padding (the size/alignment should be a multiple of 16 or 32 bytes for AVX or even 64 for futuristic AVX-512) necessary to use faster aligned moves into XMM/YMM registers (and possibly with AVX instructions in the future).
AoSoA for Random/Multi-Field Access
Unfortunately the above representation can start to lose a lot of the potential benefits if a and b are accessed frequently together, especially with a random access pattern. In such a case, a more optimal representation can start looking like this:
struct Foo1 {
char a4x32[32];
char b4x32[32];
};
... where we're now aggregating this structure. This makes it so the a and b fields are no longer so spread apart, allowing groups of 32 a and b fields to fit into a single 64-byte cache line and accessed together quickly. We can also fit 128 or 256 a or b elements now into an XMM/YMM register.
Profiling
Normally I try to avoid general wisdom advice in performance questions, but I noticed this one seems to avoid the details that someone who has profiler in hand would typically mention. So I apologize if this comes off a bit as patronizing or if a profiler is already being actively used, but I think the question warrants this section.
As an anecdote, I've often done a better job (I shouldn't!) at optimizing production code written by people who have far superior knowledge than me about computer architecture (I worked with a lot of people who came from the punch card era and can understand assembly code at a glance), and would often get called in to optimize their code (which felt really odd). It's for one simple reason: I "cheated" and used a profiler (VTune). My peers often didn't (they had an allergy to it and thought they understood hotspots just as well as a profiler and saw profiling as a waste of time).
Of course the ideal is to find someone who has both the computer architecture expertise and a profiler in hand, but lacking one or the other, the profiler can give the bigger edge. Optimization still rewards a productivity mindset which hinges on the most effective prioritization, and the most effective prioritization is to optimize the parts that truly matter the most. The profiler gives us detailed breakdowns of exactly how much time is spent and where, along with useful metrics like cache misses and branch mispredictions which even the most advanced humans typically can't predict anywhere close to as accurate as a profiler can reveal. Furthermore, profiling is often the key to discovering how the computer architecture works at a more rapid pace by chasing down hotspots and researching why they exist. For me, profiling was the ultimate entry point into better understanding how the computer architecture actually works and not how I imagined it to work. It was only then that the writings of someone as experienced in this regard as Mysticial started to make more and more sense.
Interface Design
One of the things that might start to become apparent here is that there are many optimization possibilities. The answers to this kind of question are going to be about strategies rather than absolute approaches. A lot still has to be discovered in hindsight after you try something, and still iterating towards more and more optimal solutions as you need them.
One of the difficulties here in a complex codebase is leaving enough breathing room in the interfaces to experiment and try different optimization techniques, to iterate and iterate towards faster solutions. If the interface leaves room to seek these kinds of optimizations, then we can optimize all day long and often get some marvelous results if we're measuring things properly even with a trial and error mindset.
To often leave enough breathing room in an implementation to even experiment and explore faster techniques often requires the interface designs to accept data in bulk. This is especially true if the interfaces involve indirect function calls (ex: through a dylib or a function pointer) where inlining is no longer an effective possibility. In such scenarios, leaving room to optimize without cascading interface breakages often means designing away from the mindset of receiving simple scalar parameters in favor of passing pointers to whole chunks of data (possibly with a stride if there are various interleaving possibilities). So while this is straying into a pretty broad territory, a lot of the top priorities in optimizing here are going to boil down to leaving enough breathing room to optimize implementations without cascading changes throughout your codebase, and having a profiler in hand to guide you the right way.
TL;DR
Anyway, some of these strategies should help guide you the right way. There are no absolutes here, only guides and things to try out, and always best done with a profiler in hand. Yet when processing data of this enormous scale, it's always worth remembering the image of the hungry monster, and how to most effectively feed it these appropriately-sized and packed spoonfuls of relevant data.
Let's say, you have a memory bus that's a little bit older and can deliver 10 GB/s. Now take a CPU at 2.5 GHz, and you see that you would need to handle at least four bytes per cycle to saturate the memory bus. As such, when you use the definition of
struct Foo {
char a;
char b;
char c;
char d;
}
and use all four variables in each pass through the data, your code will be CPU bound. You can't gain any speed by a denser packing.
Now, this is different when each pass only performs a trivial operation on one of the four values. In that case, you are better off with a struct of arrays:
struct Foo {
size_t count;
char* a; //a[count]
char* b; //b[count]
char* c; //c[count]
char* d; //d[count]
}
You've stated the common and ambiguous C/C++ tag.
Assuming C++, make the data private and add getters/ setters.
No, that will not cause a performance hit - providing the optimizer is turned on.
You can then change the implementation to use the alternatives without any change to your calling code - and therefore more easily finesse the implementation based on the results of the bench tests.
For the record, I'd expect the struct with bit fields as per #dbush to be most likely the fastest given your description.
Note all this is around keeping the data in cache - you may also want to see if the design of the calling algorithm can help with that.
Getting back to the question asked :
used in a tight loop;
their values are read a billion times/s, and that is the bottleneck of the program;
the whole program consists of a big array of billions of Foos;
This is a classic example of when you should write platform specific high performance code that takes time to design for each implementation platform, but the benefits outweigh that cost.
As it's the bottleneck of the entire program you don't look for a general solution, but recognize that this needs to have multiple approaches tested and timed against real data, as the best solution will be platform specific.
It is also possible, as it is a large array of billion of foos, that the OP should consider using OpenCL or OpenMP as potential solutions so as to maximize the exploitation of available resources on the runtime hardware. This is a little dependent on what you need from the data, but it's probably the most important aspect of this type of problem - how to exploit available parallelism.
But there is no single right answer to this question, IMO.
The most efficient, performance / execution, is to use the processor's word size. Don't make the processor perform extra work of packing or unpacking.
Some processors have more than one efficient size. Many ARM processors can operate in 8/32 bit mode. This means that the processor is optimized for handling 8 bit quantities or 32-bit quantities. For a processor like this, I recommend using 8-bit data types.
Your algorithm has a lot to do with the efficiency. If you are moving data or copying data you may want to consider moving data 32-bits at a time (4 8-bit quantities). The idea here is to reduce the number of fetches by the processor.
For performance, write your code to make use of registers, such as using more local variables. Fetching from memory into registers is more costly than using registers directly.
Best of all, check out your compiler optimization settings. Set your compile for the highest performance (speed) settings. Next, generate assembly language listings of your functions. Review the listing to see how the compiler generated code. Adjust your code to improve the compiler's optimization capabilities.
If what you're after is efficiency of space, then you should consider avoiding structs altogether. The compiler will insert padding into your struct representation as necessary to make its size a multiple of its alignment requirement, which might be as much as 16 bytes (but is more likely to be 4 or 8 bytes, and could after all be as little as 1 byte).
If you use a struct anyway, then which to use depends on your implementation. If #dbush's bitfield approach yields one-byte structures then it's hard to beat that. If your implementation is going to pad the representation to at least four bytes no matter what, however, then this is probably the one to use:
struct Foo {
char a;
char b;
char c;
char d;
};
Or I guess I would probably use this variant:
struct Foo {
uint8_t a;
uint8_t b;
uint8_t c;
uint8_t d;
};
Since we're supposing that your struct is taking up a minimum of four bytes, there is no point in packing the data into smaller space. That would be counter-productive, in fact, because it would also make the processor do the extra work packing and unpacking the values within.
For handling large amounts of data, making efficient use of the CPU cache provides a far greater win than avoiding a few integer operations. If your data usage pattern is at least somewhat systematic (e.g. if after accessing one element of your erstwhile struct array, you are likely to access a nearby one next) then you are likely to get a boost in both space efficiency and speed by packing the data as tightly as you can. Depending on your C implementation (or if you want to avoid implementation dependency), you might need to achieve that differently -- for instance, via an array of integers. For your particular example of four fields, each requiring two bits, I would consider representing each "struct" as a uint8_t instead, for a total of 1 byte each.
Maybe something like this:
#include <stdint.h>
#define NUMBER_OF_FOOS 1000000000
#define A 0
#define B 2
#define C 4
#define D 6
#define SET_FOO_FIELD(foos, index, field, value) \
((foos)[index] = (((foos)[index] & ~(3 << (field))) | (((value) & 3) << (field))))
#define GET_FOO_FIELD(foos, index, field) (((foos)[index] >> (field)) & 3)
typedef uint8_t foo;
foo all_the_foos[NUMBER_OF_FOOS];
The field name macros and access macros provide a more legible -- and adjustable -- way to access the individual fields than would direct manipulation of the array (but be aware that these particular macros evaluate some of their arguments more than once). Every bit is used, giving you about as good cache usage as it is possible to achieve through choice of data structure alone.
I did video decompression for a while. The fastest thing to do is something like this:
short ABCD; //use a 16 bit data type for your example
and set up some macros. Maybe:
#define GETA ((ABCD >> 12) & 0x000F)
#define GETB ((ABCD >> 8) & 0x000F)
#define GETC ((ABCD >> 4) & 0x000F)
#define GETD (ABCD & 0x000F) // no need to shift D
In practice you should try to be moving 32 bit longs or 64 bit long long because thats the native MOVE size on most modern processors.
Using a struct will always create the overhead in your compiled code of extra instructions from the base address of you struct to the field. So get away from that if you really want to tighten your loop.
Edit:
Above example gives you 4 bit values. If you really just need values of 0..3 then you can do the same things to pull out your 2 bit numbers so,,,GETA might look like this:
GETA ((ABCD >> 14) & 0x0003)
And if you are really moving billions of things things, and I don't doubt it, just fill up a 32bit variable and shift and mask your way through it.
Hope this helps.
I have a loop with the following structure :
Calculate a byte array with length k (somewhere slow)
Find if the calculated byte array matches any in a list of N byte arrays I have.
Repeat
My loop is to be called many many times (it's the main loop of my program), and I want the second step to be as fast as possible.
The naive implementation for the second step would be using memcmp:
char* calc;
char** list;
int k, n, i;
for(i = 0; i < n; i++) {
if (!memcmp(calc, list[i], k)) {
printf("Matches array %d", i);
}
}
Can you think of any faster way ? A few things :
My list is fixed at the start of my program, any precomputation on it is fine.
Let's assume that k is small (<= 64), N is moderate (around 100-1000).
Performance is the goal here, and portability is a non issue. Intrinsics/inline assembly is fine, as long as it's faster.
Here are a few thoughts that I had :
Given k<64 and I'm on x86_64, I could sort my lookup array as a long array, and do a binary search on it. O(log(n)). Even if k was big, I could sort my lookup array and do this binary search using memcmp.
Given k is small, again, I could compute a 8/16/32 bits checksum (the simplest being folding my arrays over themselves using a xor) of all my lookup arrays and use a built-in PCMPGT as in How to compare more than two numbers in parallel?. I know SSE4.2 is available here.
Do you think going for vectorization/sse is a good idea here ? If yes, what do you think is the best approach.
I'd like to say that this isn't early optimization, but performance is crucial here, I need the outer loop to be as fast as possible.
Thanks
EDIT1: It looks like http://schani.wordpress.com/tag/c-optimization-linear-binary-search-sse2-simd/ provides some interesting thoughts about it. Binary search on a list of long seems the way to go..
The optimum solution is going to depend on how many arrays there are to match, the size of the arrays, and how often they change. I would look at avoiding doing the comparisons at all.
Assuming the list of arrays to compare it to does not change frequently and you have many such arrays, I would create a hash of each array, then when you come to compare, hash the thing you are testing. Then you only need compare the hash values. With a hash like SHA256, you can rely on this both as a positive and negative indicator (i.e. the hashes matching is sufficient to say the arrays match as well as the hashes not matching being sufficient to say the arrays differ). This would work very well if you had (say) 1,000,000 arrays to compare against which hardly ever change, as calculating the hash would be faster than 1,000,000 array comparisons.
If your number of arrays is a bit smaller, you might consider a faster non-crytographic hash. For instance, a 'hash' which simply summed the bytes in an array module 256 (this is a terrible hash and you can do much better) would eliminate the need to compare (say) 255/256ths of the target array space. You could then compare only those where the so called 'hash' matches. There are well known hash-like things such as CRC-32 which are quick to calculate.
In either case you can then have a look up by hash (modulo X) to determine which arrays to actually compare.
You suggest k is small, N is moderate (i.e. about 1000). I'm guessing speed will revolve around memory cache. Not accessing 1,000 small arrays here is going to be pretty helpful.
All the above will be useless if the arrays change with a frequency similar to the comparison.
Addition (assuming you are looking at 64 bytes or similar). I'd look into a very fast non-cryptographic hash function. For instance look at: https://code.google.com/p/smhasher/wiki/MurmurHash3
It looks like 3-4 instructions per 32 bit word to generate the hash. You could then truncate the result to (say) 12 bits for a 4096 entry hash table with very few collisions (each bucket being linked list to the target arrays). This means you would look at something like about 30 instructions to calculate the hash, then one instruction per bucket entry (expected value 1) to find the list item, then one manual compare per expected hit (that would be between 0 and 1). So rather than comparing 1000 arrays, you would compare between 0 and 1 arrays, and generate one hash. If you can't compare 999 arrays in 30-ish instructions (I'm guessing not!) this is obviously a win.
We can assume that my stuff fits in 64bits, or even 32bits. If it
wasn't, I could hash it so it could. But now, what's the fastest way
to find whether my hash exists in the list of precomputed hashes ?
This is sort of a meta-answer, but... if your question boils down to: how can I efficiently find whether a certain 32-bit number exists in a list of other 32-bit numbers, this is a problem IP routers deal with all the time, so it might be worth looking into the networking literature to see if there's something you can adapt from their algorithms. e.g. see http://cit.mak.ac.ug/iccir/downloads/SREC_07/K.J.Poornaselvan1,S.Suresh,%20C.Divya%20Preya%20and%20C.G.Gayathri_07.pdf
(Although, I suspect they are optimized for searching through larger numbers of items than your use case..)
can you do an XOR instead of memcmp ?
or caclulate hash of each element in the array and sort it search for the hash
but hash will take more time .unless you can come up with a faster hash
Another way is to pre-build a tree from your list and use tree search.
for examples, with list:
aaaa
aaca
acbc
acca
bcaa
bcca
caca
we can get a tree like this
root
-a
--a
---a
----a
---c
----a
--c
---b
----c
---c
----a
-b
--c
---a
----a
---c
----a
-c
--a
---c
----a
Then do binary search on each level of the tree
I'm looking for the fastest possible way to permutate bits in a 64 bit integer.
Given a table called "array" corresponding to a permutations array, meaning it has a size of 64 and filled with unique numbers (i.e. no repetition) ranging from 0 to 63, corresponding to bit positions in a 64 bit integer, I can permutate bits this way
bit = GetBitAtPos(integer_, array[i]);
SetBitAtPos(integer_, array[i], GetBitAtPos(integer_, i));
SetBitAtPos(integer_, i, bit);
(by looping i from 0 to 63)
GetBitAtPos being
GetBitAtPos(integer_, pos) { return (integer >>) pos & 1 }
Setbitatpos is also founded on the same principle (i.e. using C operators),
under the form SetBitAtPos(integer, position, bool_bit_value)
I was looking for a faster way, if possible, to perform this task. I'm open to any solution, including inline assembly if necessary. I have difficulty to figure a better way than this, so I thought I'd ask.
I'd like to perform such a task to hide data in a 64 bit generated integer (where the 4 first bit can reveal informations). It's a bit better than say a XOR mask imo (unless I miss something), mostly if someone tries to find a correlation.
It also permits to do the inverse operation to not lose the precious bits...
However I find the operation to be a bit costly...
Thanks
Since the permutation is constant, you should be able to come up with a better way than moving the bits one by one (if you're OK with publishing your secret permutation, I can have a go at it). The simplest improvement is moving bits that have the same distance (that can be a modular distance because you can use rotates) between them in the input and output at the same time. This is a very good methods if there are few such groups.
If that didn't work out as well as you'd hoped, see if you can use bit_permute_steps to move all or most of the bits. See the rest of that site for more ideas.
If you can use PDEP and PEXT, you can move bits in groups where the distance between bits can arbitrarily change (but their order can not). It is, afaik, unknown how fast they will be though (and they're not available yet).
The best method is probably going to be a combination of these and other tricks mentioned in other answers.
There are too many possibilities to explore them all, really, so you're probably not going to find the best way to do the permutation, but using these ideas (and the others that were posted) you can doubtlessly find a better what than you're currently using.
PDEP and PEXT have been available for a while now so their performance is known, at 3 cycle latency and 1/cycle throughput they're faster than most other useful permutation primitives (except trivial ones).
Split your bits into subsets where this method works:
Extracting bits with a single multiplication
Then combine the results using bitwise OR.
For 64-bit number I believe the problem (of finding best algorithm) may be unsolvable due to huge amount of possibilities. One of the most scalable and easiest to automatize would be look up table:
result = LUT0[ value & 0xff] +
LUT1[(value >> 8) & 0xff] +
LUT2[(value >> 16) & 0xff] + ...
+ LUT7[(value >> 56) & 0xff];
Each LUT entry must be 64-bit wide and it just spreads each 8 bits in a subgroup to the full range of 64 possible bins. This configuration uses 16k of memory.
The scalability comes from the fact that one can use any number of look up tables (practical range from 3 to 32?). This method is vulnerable to cache misses and it can't be parallelized (for large table sizes at least).
If there are certain symmetries, there are some clever trick available --
e.g. swapping two bits in Intel:
test eax, (1<<BIT0 | 1<<BIT1)
jpe skip:
xor eax, (1<<BIT0 | 1<<BIT1)
skip:
This OTOH is highly vulnerable to branch mispredictions.
Is there a historical reason or something ? I've seen quite a few times something like char foo[256]; or #define BUF_SIZE 1024. Even I do mostly only use 2n sized buffers, mostly because I think it looks more elegant and that way I don't have to think of a specific number. But I'm not quite sure if that's the reason most people use them, more information would be appreciated.
There may be a number of reasons, although many people will as you say just do it out of habit.
One place where it is very useful is in the efficient implementation of circular buffers, especially on architectures where the % operator is expensive (those without a hardware divide - primarily 8 bit micro-controllers). By using a 2^n buffer in this case, the modulo, is simply a case of bit-masking the upper bits, or in the case of say a 256 byte buffer, simply using an 8-bit index and letting it wraparound.
In other cases alignment with page boundaries, caches etc. may provide opportunities for optimisation on some architectures - but that would be very architecture specific. But it may just be that such buffers provide the compiler with optimisation possibilities, so all other things being equal, why not?
Cache lines are usually some multiple of 2 (often 32 or 64). Data that is an integral multiple of that number would be able to fit into (and fully utilize) the corresponding number of cache lines. The more data you can pack into your cache, the better the performance.. so I think people who design their structures in that way are optimizing for that.
Another reason in addition to what everyone else has mentioned is, SSE instructions take multiple elements, and the number of elements input is always some power of two. Making the buffer a power of two guarantees you won't be reading unallocated memory. This only applies if you're actually using SSE instructions though.
I think in the end though, the overwhelming reason in most cases is that programmers like powers of two.
Hash Tables, Allocation by Pages
This really helps for hash tables, because you compute the index modulo the size, and if that size is a power of two, the modulus can be computed with a simple bitwise-and or & rather than using a much slower divide-class instruction implementing the % operator.
Looking at an old Intel i386 book, and is 2 cycles and div is 40 cycles. A disparity persists today due to the much greater fundamental complexity of division, even though the 1000x faster overall cycle times tend to hide the impact of even the slowest machine ops.
There was also a time when malloc overhead was occasionally avoided at great length. Allocation's available directly from the operating system would be (still are) a specific number of pages, and so a power of two would be likely to make the most use of the allocation granularity.
And, as others have noted, programmers like powers of two.
I can think of a few reasons off the top of my head:
2^n is a very common value in all of computer sizes. This is directly related to the way bits are represented in computers (2 possible values), which means variables tend to have ranges of values whose boundaries are 2^n.
Because of the point above, you'll often find the value 256 as the size of the buffer. This is because it is the largest number that can be stored in a byte. So, if you want to store a string together with a size of the string, then you'll be most efficient if you store it as: SIZE_BYTE+ARRAY, where the size byte tells you the size of the array. This means the array can be any size from 1 to 256.
Many other times, sizes are chosen based on physical things (for example, the size of the memory an operating system can choose from is related to the size of the registers of the CPU etc) and these are also going to be a specific amount of bits. Meaning, the amount of memory you can use will usually be some value of 2^n (for a 32bit system, 2^32).
There might be performance benefits/alignment issues for such values. Most processors can access a certain amount of bytes at a time, so even if you have a variable whose size is let's say) 20 bits, a 32 bit processor will still read 32 bits, no matter what. So it's often times more efficient to just make the variable 32 bits. Also, some processors require variables to be aligned to a certain amount of bytes (because they can't read memory from, for example, addresses in the memory that are odd). Of course, sometimes it's not about odd memory locations, but locations that are multiples of 4, or 6 of 8, etc. So in these cases, it's more efficient to just make buffers that will always be aligned.
Ok, those points came out a bit jumbled. Let me know if you need further explanation, especially point 4 which IMO is the most important.
Because of the simplicity (read also cost) of base 2 arithmetic in electronics: shift left (multiply by 2), shift right (divide by 2).
In the CPU domain, lots of constructs revolve around base 2 arithmetic. Busses (control & data) to access memory structure are often aligned on power 2. The cost of logic implementation in electronics (e.g. CPU) makes for arithmetics in base 2 compelling.
Of course, if we had analog computers, the story would be different.
FYI: the attributes of a system sitting at layer X is a direct consequence of the server layer attributes of the system sitting below i.e. layer < x. The reason I am stating this stems from some comments I received with regards to my posting.
E.g. the properties that can be manipulated at the "compiler" level are inherited & derived from the properties of the system below it i.e. the electronics in the CPU.
I was going to use the shift argument, but could think of a good reason to justify it.
One thing that is nice about a buffer that is a power of two is that circular buffer handling can use simple ands rather than divides:
#define BUFSIZE 1024
++index; // increment the index.
index &= BUFSIZE; // Make sure it stays in the buffer.
If it weren't a power of two, a divide would be necessary. In the olden days (and currently on small chips) that mattered.
It's also common for pagesizes to be powers of 2.
On linux I like to use getpagesize() when doing something like chunking a buffer and writing it to a socket or file descriptor.
It's makes a nice, round number in base 2. Just as 10, 100 or 1000000 are nice, round numbers in base 10.
If it wasn't a power of 2 (or something close such as 96=64+32 or 192=128+64), then you could wonder why there's the added precision. Not base 2 rounded size can come from external constraints or programmer ignorance. You'll want to know which one it is.
Other answers have pointed out a bunch of technical reasons as well that are valid in special cases. I won't repeat any of them here.
In hash tables, 2^n makes it easier to handle key collissions in a certain way. In general, when there is a key collission, you either make a substructure, e.g. a list, of all entries with the same hash value; or you find another free slot. You could just add 1 to the slot index until you find a free slot; but this strategy is not optimal, because it creates clusters of blocked places. A better strategy is to calculate a second hash number h2, so that gcd(n,h2)=1; then add h2 to the slot index until you find a free slot (with wrap around). If n is a power of 2, finding a h2 that fulfills gcd(n,h2)=1 is easy, every odd number will do.